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2023 was, by far, the most prolific year in the history of NLP. This period saw the emergence of ChatGPT alongside numerous other Large Language Models, both open-source and proprietary.
At the same time, fine-tuning LLMs became way easier and the competition among cloud providers for the GenAI offering intensified significantly.
Interestingly, the demand for personalized and fully operational RAGs also skyrocketed across various industries, with each client eager to have their own tailored solution.
Speaking of this last point, creating fully functioning RAGs, in today’s post we will discuss a paper that reviews the current state of the art of building those systems.
Without further ado, let’s have a look 🔍
I started reading this piece during my vacation
and it’s a must.
It covers everything you need to know about the RAG framework and its limitations. It also lists modern techniques to boost its performance in retrieval, augmentation, and generation.
The ultimate goal behind these techniques is to make this framework ready for scalability and production use, especially for use cases and industries where answer quality matters *a lot*.
I won’t discuss everything in this paper, but here are the key ideas that, in my opinion, would make your RAG more efficient.
As the data we index determines the quality of the RAG’s answers, the first task is to curate it as much as possible before ingesting it. (Garbage in, garbage out still applies here)
You can do this by removing duplicate/redundant information, spotting irrelevant documents, and checking for fact accuracy (if possible).
If the maintainability of the RAG matters, you also need to add mechanisms to refresh…
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